The third edition of the "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" workshop featured four data-impaired challenges, focusing on addressing the limitations of data availability in training deep learning models for computer vision tasks. The challenges comprised of four distinct data-impaired tasks, where participants were required to train models from scratch using a reduced number of training samples. The primary objective was to encourage novel approaches that incorporate relevant inductive biases to enhance the data efficiency of deep learning models. To foster creativity and exploration, participants were strictly prohibited from utilizing pre-trained checkpoints and other transfer learning techniques. Significant advancements were made compared to the provided baselines, where winning solutions surpassed the baselines by a considerable margin in all four tasks. These achievements were primarily attributed to the effective utilization of extensive data augmentation policies, model ensembling techniques, and the implementation of data-efficient training methods, including self-supervised representation learning. This report highlights the key aspects of the challenges and their outcomes.

翻译：第三届"VIPriors：面向数据高效深度学习的视觉归纳先验"研讨会举办了四项数据受限挑战，旨在解决计算机视觉任务中深度学习模型训练时数据可用性受限的问题。这些挑战包含四项不同的数据受限任务，要求参与者使用减少的训练样本数量从头训练模型。主要目标是鼓励融合相关归纳偏置的创新方法，以提升深度学习模型的数据效率。为激发创造性与探索性，参与者被严格禁止使用预训练检查点及其他迁移学习技术。与提供的基线相比，所有四项任务均取得了显著进展，获胜解决方案的表现大幅超越基线。这些成就主要归功于对广泛数据增强策略、模型集成技术以及数据高效训练方法（包括自监督表征学习）的有效运用。本报告重点阐述了各项挑战的关键方面及其成果。